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Our second analysis of the spatio-temporal reasoning method in the Electric Box computer game domain verifies the success of our approach.At our site you will find many other games of different genres, from racing and action to the logic and gambling apk games. This is promising as gathering spatio-temporal information does not require prior knowledge about relations. We have also demonstrated that our learning method which incorporates both spatial and temporal information gives close results to that of the knowledge-based approach. Our analysis reveals that if a knowledge base about relations is provided, most of the interactions can be learned. We analyze the results of our reasoning system on four different input types: a knowledge base of relations spatial information temporal information and spatio-temporal information from the environment. Tutorials of the game are used to train the system. We take The Incredible Machine game (TIM) as the main testbed to analyze our system. Experience gained through learning is to be used for achieving goals by these objects. Furthermore, we propose a spatio-temporal reasoning based learning method for reasoning about interactions among objects. We use an existing system to learn the models of objects and further extend it to model more complex behaviors. In this paper, we introduce an automated reasoning system for learning object behaviors and interactions through the observation of event sequences. The development of proposed method into an autonomous planning agent with real time task orientation is left to work in future. PDDL is used to model agent's percept sequence with preconditions on actions and its effects. Problem solving approach using Planning Domain Definition Language (PDDL) is significant when domain definitions involving agent's preconditions, actions and effects are required to model the percept sequence. Spatio-temporal reasoning based automated planning is proposed in which object interaction based on spatial relations in temporal scale is a state, representing an event and transition among the states are modeled using Finite State Machine (FSM) to extract the action sequence that accomplishes the task. The agent in an environment act on percept sequence to accomplish the states those lead to the goal state. The essence of action sequence relies on representation of agent's perception towards the goal. An agent is an entity that perceives the environment. Knowledge Representation and Reasoning (KRR) in the domain of planning involve knowledge computing on expressiveness of action sequence that leads to desired goal state.